
from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


lowercase    :   Optional[Any]		       =				logging.get_logger(__name__)


lowercase    :   Optional[Any]		       =				{
    'Salesforce/codegen-350M-nl': 'https://huggingface.co/Salesforce/codegen-350M-nl/resolve/main/config.json',
    'Salesforce/codegen-350M-multi': 'https://huggingface.co/Salesforce/codegen-350M-multi/resolve/main/config.json',
    'Salesforce/codegen-350M-mono': 'https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/config.json',
    'Salesforce/codegen-2B-nl': 'https://huggingface.co/Salesforce/codegen-2B-nl/resolve/main/config.json',
    'Salesforce/codegen-2B-multi': 'https://huggingface.co/Salesforce/codegen-2B-multi/resolve/main/config.json',
    'Salesforce/codegen-2B-mono': 'https://huggingface.co/Salesforce/codegen-2B-mono/resolve/main/config.json',
    'Salesforce/codegen-6B-nl': 'https://huggingface.co/Salesforce/codegen-6B-nl/resolve/main/config.json',
    'Salesforce/codegen-6B-multi': 'https://huggingface.co/Salesforce/codegen-6B-multi/resolve/main/config.json',
    'Salesforce/codegen-6B-mono': 'https://huggingface.co/Salesforce/codegen-6B-mono/resolve/main/config.json',
    'Salesforce/codegen-16B-nl': 'https://huggingface.co/Salesforce/codegen-16B-nl/resolve/main/config.json',
    'Salesforce/codegen-16B-multi': 'https://huggingface.co/Salesforce/codegen-16B-multi/resolve/main/config.json',
    'Salesforce/codegen-16B-mono': 'https://huggingface.co/Salesforce/codegen-16B-mono/resolve/main/config.json',
}

class __snake_case						( lowerCAmelCase	):
    _a						:		List[str]=							"codegen"
    _a						:		List[str]=							{
        "max_position_embeddings": "n_positions",
        "hidden_size": "n_embd",
        "num_attention_heads": "n_head",
        "num_hidden_layers": "n_layer",
    }
    def __init__(    self						,snake_case=50400						,snake_case=2048						,snake_case=2048						,snake_case=4096						,snake_case=28						,snake_case=16						,snake_case=64						,snake_case=None						,snake_case="gelu_new"						,snake_case=0.0						,snake_case=0.0						,snake_case=0.0						,snake_case=1e-5						,snake_case=0.02						,snake_case=True						,snake_case=50256						,snake_case=50256						,snake_case=False						,**snake_case						,):



           '''simple docstring'''







           lowercase				:	List[str]			      = vocab_size
           lowercase				:	Dict			      = n_ctx
           lowercase				:	Dict			      = n_positions
           lowercase				:	Optional[Any]			      = n_embd
           lowercase				:	List[Any]			      = n_layer
           lowercase				:	Optional[Any]			      = n_head
           lowercase				:	int			      = n_inner
           lowercase				:	Any			      = rotary_dim
           lowercase				:	List[str]			      = activation_function
           lowercase				:	List[str]			      = resid_pdrop
           lowercase				:	Union[str, Any]			      = embd_pdrop
           lowercase				:	Dict			      = attn_pdrop
           lowercase				:	Optional[int]			      = layer_norm_epsilon
           lowercase				:	List[str]			      = initializer_range
           lowercase				:	str			      = use_cache

           lowercase				:	List[Any]			      = bos_token_id
           lowercase				:	Tuple			      = eos_token_id

           super().__init__(
               bos_token_id=_A						,eos_token_id=_A						,tie_word_embeddings=_A						,**_A )






class __snake_case						( lowerCAmelCase	):
    def __init__(    self						,snake_case						,snake_case = "default"						,snake_case = None						,snake_case = False						,):



           '''simple docstring'''







           super().__init__(_A						,task=_A						,patching_specs=_A						,use_past=_A )
           if not getattr(self._config						,"""pad_token_id"""						,_A ):
                  # TODO: how to do that better?
                  lowercase				:	List[str]			      = 0
    @property
    def  _SCREAMING_SNAKE_CASE			(    self ):



           '''simple docstring'''







           lowercase				:	List[Any]			      = OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}} )
           if self.use_past:
                  self.fill_with_past_key_values_(_A						,direction="""inputs""" )
                  lowercase				:	int			      = {0: '''batch''', 1: '''past_sequence + sequence'''}
           else:
                  lowercase				:	List[Any]			      = {0: '''batch''', 1: '''sequence'''}

           return common_inputs
    @property
    def  _SCREAMING_SNAKE_CASE			(    self ):



           '''simple docstring'''







           return self._config.n_layer
    @property
    def  _SCREAMING_SNAKE_CASE			(    self ):



           '''simple docstring'''







           return self._config.n_head
    def  _SCREAMING_SNAKE_CASE			(    self						,snake_case						,snake_case = -1						,snake_case = -1						,snake_case = False						,snake_case = None						,):



           '''simple docstring'''







           lowercase				:	int			      = super(_A						,self ).generate_dummy_inputs(
               _A						,batch_size=_A						,seq_length=_A						,is_pair=_A						,framework=_A )

           # We need to order the input in the way they appears in the forward()
           lowercase				:	Dict			      = OrderedDict({"""input_ids""": common_inputs["""input_ids"""]} )

           # Need to add the past_keys
           if self.use_past:
                  if not is_torch_available():
                         raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed.""" )
                  else:
                         import torch

                         lowercase				:	Optional[int]			      = common_inputs['''input_ids'''].shape
                         # Not using the same length for past_key_values
                         lowercase				:	int			      = seqlen + 2
                         lowercase				:	List[str]			      = (
                             batch,
                             self.num_attention_heads,
                             past_key_values_length,
                             self._config.hidden_size // self.num_attention_heads,
                         )
                         lowercase				:	Optional[int]			      = [
                             (torch.zeros(_A ), torch.zeros(_A )) for _ in range(self.num_layers )
                         ]

           lowercase				:	Optional[int]			      = common_inputs['''attention_mask''']
           if self.use_past:
                  lowercase				:	Tuple			      = ordered_inputs['''attention_mask'''].dtype
                  lowercase				:	List[Any]			      = torch.cat(
                      [ordered_inputs["""attention_mask"""], torch.ones(_A						,_A						,dtype=_A )]						,dim=1 )

           return ordered_inputs
    @property
    def  _SCREAMING_SNAKE_CASE			(    self ):



           '''simple docstring'''







           return 13








'''simple docstring'''





import warnings

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCamelCase						:						Optional[Any]  =					logging.get_logger(__name__)

_UpperCamelCase						:						Dict  =					{
    'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json',
}


class  snake_case__      (					UpperCamelCase):
					a_        =					"mvp"
					a_        =					["past_key_values"]
					a_        =					{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}




					def __init__(  self  :		Any				, _A  :		List[str]=5_02_67				, _A  :		Dict=10_24				, _A  :		List[str]=12				, _A  :		Dict=40_96				, _A  :		Optional[int]=16				, _A  :		Optional[Any]=12				, _A  :		int=40_96				, _A  :		Dict=16				, _A  :		Union[str, Any]=0.0				, _A  :		Optional[Any]=0.0				, _A  :		int="gelu"				, _A  :		Union[str, Any]=10_24				, _A  :		int=0.1				, _A  :		Optional[Any]=0.0				, _A  :		Optional[Any]=0.0				, _A  :		Optional[Any]=0.02				, _A  :		Optional[Any]=0.0				, _A  :		str=False				, _A  :		List[Any]=True				, _A  :		Optional[int]=1				, _A  :		Tuple=0				, _A  :		Dict=2				, _A  :		Dict=True				, _A  :		int=2				, _A  :		Tuple=2				, _A  :		List[str]=False				, _A  :		Any=1_00				, _A  :		Union[str, Any]=8_00				, **_A  :		List[str]				, )    ->							int:
								UpperCAmelCase_      :       Union[str, Any]          =       vocab_size
								UpperCAmelCase_      :       int          =       max_position_embeddings
								UpperCAmelCase_      :       Dict          =       d_model
								UpperCAmelCase_      :       Dict          =       encoder_ffn_dim
								UpperCAmelCase_      :       Optional[Any]          =       encoder_layers
								UpperCAmelCase_      :       Tuple          =       encoder_attention_heads
								UpperCAmelCase_      :       List[Any]          =       decoder_ffn_dim
								UpperCAmelCase_      :       List[Any]          =       decoder_layers
								UpperCAmelCase_      :       Dict          =       decoder_attention_heads
								UpperCAmelCase_      :       str          =       dropout
								UpperCAmelCase_      :       Optional[Any]          =       attention_dropout
								UpperCAmelCase_      :       Optional[Any]          =       activation_dropout
								UpperCAmelCase_      :       Union[str, Any]          =       activation_function
								UpperCAmelCase_      :       Union[str, Any]          =       init_std
								UpperCAmelCase_      :       Optional[int]          =       encoder_layerdrop
								UpperCAmelCase_      :       Optional[int]          =       decoder_layerdrop
								UpperCAmelCase_      :       Dict          =       classifier_dropout
								UpperCAmelCase_      :       List[Any]          =       use_cache
								UpperCAmelCase_      :       Dict          =       encoder_layers
								UpperCAmelCase_      :       Tuple          =       scale_embedding  # scale factor will be sqrt(d_model) if True
								UpperCAmelCase_      :       int          =       use_prompt
								UpperCAmelCase_      :       Tuple          =       prompt_length
								UpperCAmelCase_      :       str          =       prompt_mid_dim

								super().__init__(
								    pad_token_id=_A				, bos_token_id=_A				, eos_token_id=_A				, is_encoder_decoder=_A				, decoder_start_token_id=_A				, forced_eos_token_id=_A				, **_A				, )

								if self.forced_bos_token_id is None and kwargs.get('''force_bos_token_to_be_generated'''				, _A      ):
											UpperCAmelCase_      :       Optional[Any]          =       self.bos_token_id
											warnings.warn(
											    F"Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. "
											    '''The config can simply be saved and uploaded again to be fixed.'''      )


